All posts tagged Norm

May 12, 2011

Positive evaluation of Let’sMT! project’s 1st year

SemLab and the other partners of the Let’sMT project have met in Luxembourg to present the results of the first year of the project.

Presentations were given for each of the work packages as well as a live demo of the already operational translation system, the translation widget and browser plugin.

The preliminary response from the evaluators was very positive and the project will continue as planned.

 

May 25, 2010

NORM – News Optimised Risk Management project started

The NORM project has now officially started. NORM, short for News Optimised Risk management, is a project commissioned by the European Union to research the effects of financial news on market risk predictions. In order to study this, the project will use SemLab’s ViewerPro system for automated semantic news analysis, together with state of the art risk modelling techniques.

 

January 20, 2010

Semlab to research News Optimised Risk Management (NORM)

The European Union has approved our NORM proposal. Semlab and a consortium of international partners will start researching News Optimised Risk Management.

In today’s chaotic financial climate, systems for predicting market behaviour and attitudes of financial professionals are under scrutiny. Current market risk assessment characteristics disregard market information that is available from additional sources like, for example, financial news. There are whole new possibilities for producing meaningful market behaviour models by incorporating behavioural and quantitative finance, using the latest techniques and powerful modelling tools. The prevailing market environment can (to some extent) be captured by key innovative techniques of news analytics that quantify news sentiments. The emergence and impact of such behavioural finance is illustrated by the 4-5 Nobel Prizes for Economics awarded in this field in recent years.

This project aims to enhance market risk assessment metrics by using semantically analysed news-based information. This will compensate for inflexibility of existing models with regard to strong market fluctuations or market instability and give more dynamic, more reliable market risk estimation.